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OverviewDramatic advances in computing power enable simulation of DNA sequences generated by complex microevolutionary scenarios that include mutation, population structure, natural selection, meiotic recombination, demographic change, and explicit spatial geographies. Although retrospective, coalescent simulation is computationally efficient—and covered here—the primary focus of this book is forward-in-time simulation, which frees us to simulate a wider variety of realistic microevolutionary models. The book walks the reader through the development of a forward-in-time evolutionary simulator dubbed FORward Time simUlatioN Application (FORTUNA). The capacity of FORTUNA grows with each chapter through the addition of a new evolutionary factor to its code. Each chapter also reviews the relevant theory and links simulation results to key evolutionary insights. The book addresses visualization of results through development of R code and reference to more than 100 figures. All code discussedin the book is freely available, which the reader may use directly or modify to better suit his or her own research needs. Advanced undergraduate students, graduate students, and professional researchers will all benefit from this introduction to the increasingly important skill of population genetic simulation. Full Product DetailsAuthor: Ryan J. HaaslPublisher: Springer Nature Switzerland AG Imprint: Springer Nature Switzerland AG Edition: 1st ed. 2022 Weight: 0.664kg ISBN: 9783030973803ISBN 10: 3030973808 Pages: 313 Publication Date: 02 September 2022 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsAmended draft Table of Contents 1. Introduction and relevance a. Who this book benefits b. Required background c. Review of population genetic/genomic simulation resources d. When to write your own simulations 2. Retrospective and prospective simulation a. Retrospective, coalescent simulation b. Prospective simulation c. Individual-based models 3. Data structures and computational efficiency a. Data structures b. Computer clusters c. Graphical processor units (GPU) programming Part I. Simulating the five factors that affect population dynamics and genetic diversity 4. Mutation a. Background and theory b. The bitset as a data structure for storing genetic sequence data c. Writing individual and population classes d. Common types of mutation i. Point mutations ii. Indels iii. Microsatellites iv. AFLPs e. Haplotypes 5. Population size and genetic drift a. Background and theory b. Fixation of alleles c. Demographic change: expansions and bottlenecks 6. Migration and population structure a. Background and theory b. Panmixia c. Isolation by barrier d. Isolation by distance e. Admixture f. Metapopulations 7. Meiotic recombination a. Background and theory b. Unlinked loci and independent assortment c. Linked loci and crossing-over d. Linked loci and gene conversion 8. Natural selection a. Background and theory b. Fitness c. Viability and fecundity selection d. Positive natural selection e. Purifying natural selection and background selection f. Frequency-dependent selection g. Assortative mating h. Selection on a protein-coding gene Part II. Adding biological and ecological realism 9. Implementing all five factors simultaneously a. A generation function: the order of things b. Birth and death c. Overlapping vs. non-overlapping generations d. Using coalescent simulation to obtain random starting populations 10. Modeling different life histories a. Ploidy b. Monoecious, dioecious, and hermaphroditic species 11. Spatially-explicit simulation a. Neutral evolution b. The impact of landscape on dispersal c. The impact of environment on fitness Part III. Statistical inference in population genetics 12. Calculating summary statistics and visualization a. Sequence-based summary statistics b. Locus-specific summary statistics c. Null distributions d. Visualizing simulated and empirical data 13. Approximate Bayesian computation: preliminaries a. Statistical and historical background b. Parameters c. Prior distributions d. Sufficient summary statistics e. Tolerance level f. The relevance of data type: SNPs, microsatellites, AFLPs 14. Approximate Bayesian computation: implementation a. Rejection algorithms b. Regression-based algorithms c. Markov Chain Monte Carlo algorithms d. Sequential Monte Carlo algorithms e. Hierarchical models f. Model selection g. Parameter estimation h. Marginal, joint, and conditional distributions i. Posterior predictive distributions and model validation j. When simulation is costly: Approximate approximate Bayesian computation Part IV. In-depth examples 15. Comparing simulated genetic data to 1000 Genomes data 16. The spread of the invasive species Japanese hops in the Upper Midwest, USA Appendices C++: Review and reference R: Review and referenceReviewsAuthor InformationRyan J. Haasl is an Associate Professor of Biology at the University of Wisconsin-Platteville. He holds an M.A. in Entomology from the University of Kansas and a Ph.D. in Genetics from the University of Wisconsin-Madison. His research focuses on the use of simulation and statistical computing to explore favorite topics such as natural selection targeting microsatellites, phylogenomics, and the consolidation of microevolutionary dynamics and macroevolutionary pattern. He is passionate about teaching genetics and evolutionary biology to undergraduate students and fostering public literacy in the biological sciences through outreach. Tab Content 6Author Website:Countries AvailableAll regions |
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